AI Agent Security & Prompt Injection
Secure AI agents with practical controls—prompt injection defenses, permission-aware retrieval, tool restrictions, logging, approvals, and testing.
AI agents expand your attack surface. Once an agent can retrieve internal documents or call tools that interact with enterprise systems, risk shifts from "bad text output" to "system integrity." Prompt injection is a major risk, but the more dangerous pattern is indirect injection: malicious instructions embedded in documents, tickets, or retrieved content that the agent treats as authoritative.
A secure agent system starts with threat modeling and least privilege. Retrieval must be permission-aware. Tools must be constrained, parameterized, and logged. Sensitive actions should require approvals until evaluation demonstrates safety. Logging is not optional; without traceability you cannot debug incidents or prove compliance.
Security is also deeply tied to evaluation and monitoring. Many failures are regressions: a prompt or tool change introduces new leakage paths. So you need adversarial test cases, monitoring for suspicious tool call patterns, and clear escalation behavior.
Threat model for enterprise agents
Prompt injection and indirect injection
Data exfiltration paths (RAG + tools)
Controls (least privilege, allowlists, approvals, logs)
Testing and monitoring for security regressions
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